Finding Extract Method Refactoring Opportunities by Analyzing Development History

2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) Pub Date : 2017-07-01 DOI:10.1109/COMPSAC.2017.129

Ayaka Imazato, Yoshiki Higo, Keisuke Hotta, S. Kusumoto

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引用次数: 3

Abstract

Refactoring is an important technique to improve maintainability of software, and developers often use this technique during a development process. Before now, researchers have proposed some techniques finding refactoring opportunities for developers. Finding refactoring opportunities means identifying locations to be refactored. However, there are no specific criteria for developers to determine where they should refactor because the criteria differ from project to project and from developer to developer. In this study, we propose a technique to find refactoring opportunities in source code by using machine learning techniques. Machine learning techniques enable to flexibly find refactoring opportunities by the characteristics of target projects and developers. Our proposed technique learns information on the features of refactorings conducted in the past. Then, based on this information, it suggests some refactorings on given the source code to developers. We investigated three research questions with five open source projects. As a result, we confirmed that the proposed technique was able to find refactorings with high accuracy.

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通过分析开发历史发现提取方法重构的机会

重构是提高软件可维护性的一项重要技术，开发人员经常在开发过程中使用这项技术。在此之前，研究人员已经提出了一些为开发人员寻找重构机会的技术。寻找重构机会意味着确定要重构的位置。然而，对于开发人员来说，没有特定的标准来确定他们应该在哪里重构，因为标准因项目而异，因开发人员而异。在这项研究中，我们提出了一种通过使用机器学习技术在源代码中找到重构机会的技术。机器学习技术可以根据目标项目和开发人员的特征灵活地找到重构机会。我们提出的技术学习了过去进行的重构的特征信息。然后，根据这些信息，它建议对给定的源代码进行一些重构。我们调查了五个开源项目的三个研究问题。结果，我们证实了所提出的技术能够高精度地找到重构。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)

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